Abstract

Subspace and projected clustering have emerged as a possible solution to the challenges associated with clustering in high-dimensional data. Numerous subspace and projected clustering techniques have been proposed in the literature. A comprehensive evaluation of their advantages and disadvantages is urgently needed. In this paper, we evaluate systematically state-of-the-art subspace and projected clustering techniques under a wide range of experimental settings. We discuss the observed performance of the compared techniques, and we make recommendations regarding what type of techniques are suitable for what kind of problems.